class_docstr: Data type that is fed into and yielded from agent connectors.
  
  Args:
      env_id: ID of the environment.
      agent_id: ID to help identify the agent from which the data is received.
      input_dict: Input data that was passed into the policy.
          Sometimes output must be adapted based on the input, for example
          action masking. So the entire input data structure is provided here.
      output: An object of PolicyOutputType. It is is composed of the
          action output, the internal state output, and additional data fetches.
code: def __init__(
      self,
      env_id: str,
      agent_id: str,
      input_dict: TensorStructType,
      output: PolicyOutputType,
  ):
      self.env_id = env_id
      self.agent_id = agent_id
      self.input_dict = input_dict
      self.output = output
  
file_path: ray/python/ray/rllib/utils/typing.py
imports: from typing import TYPE_CHECKING, Any, Callable, Dict, Hashable, List, Optional, Tuple, Type, TypeVar, Union
  import numpy
  import gymnasium
  from ray.rllib.utils.annotations import ExperimentalAPI
label_desc: Use image node_id 1 to create a new ActionConnectorDataType object  with example: obj = ActionConnectorDataType(env_id, agent_id, input_dict, output)